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Adaptive Potential Fields Model for Solving Distributed Area Coverage Problem in Swarm Robotics

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Advances in Swarm Intelligence (ICSI 2017)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 10386))

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Abstract

Complete coverage of a given region has become a fundamental problem addressed in the field of swarm robots. Currently available approaches to the coverage problem are typically of computational complexity, and are manually specified with different map settings, which are not scalable and flexible. To address these shortcomings, this paper describes an efficient distributed approach based on potential fields method and self-adaptive control. It makes no assumptions about prior knowledge on global map, and need few manual intervention during execution. Although the motion policy of each robot is very simple, efficient coverage behavior is achieved at team level. We evaluate the approach against a traditional rule-based method and pheromone method under different target area scenarios. It shows state-of-the-art performance, both in the percentage of coverage and the degree of connectivity.

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References

  1. Agmon, N., Hazon, N., Kaminka, G.A.: Constructing spanning trees for efficient multi-robot coverage. In: Proceedings 2006 IEEE International Conference on Robotics and Automation. ICRA 2006, pp. 1698–1703. IEEE (2006)

    Google Scholar 

  2. Agmon, N., Kaminka, G.A., Kraus, S.: Multi-robot adversarial patrolling: facing a full-knowledge opponent. J. Artif. Intell. Res. 42, 887–916 (2011)

    MathSciNet  MATH  Google Scholar 

  3. Bayındır, L.: A review of swarm robotics tasks. Neurocomputing 172, 292–321 (2016)

    Article  Google Scholar 

  4. Hert, S., Tiwari, S., Lumelsky, V.: A terrain-covering algorithm for an AUV. Auton. Robots 3(2), 91–119 (1996)

    Article  Google Scholar 

  5. Kantaros, Y., Thanou, M., Tzes, A.: Distributed coverage control for concave areas by a heterogeneous robot-swarm with visibility sensing constraints. Automatica 53, 195–207 (2015)

    Article  MathSciNet  Google Scholar 

  6. Kernbach, S.: Structural Self-Organization in Multi-agents and Multi-robotic Systems. Logos Verlag Berlin GmbH, Berlin (2008)

    Google Scholar 

  7. Li, J., Tan, Y.: The multi-target search problem with environmental restrictions in swarm robotics. In: 2014 IEEE International Conference on Robotics and Biomimetics (ROBIO), pp. 2685–2690. IEEE (2014)

    Google Scholar 

  8. McLurkin, J., Smith, J.: Distributed algorithms for dispersion in indoor environments using a swarm of autonomous mobile robots. In: 7th International Symposium on Distributed Autonomous Robotic Systems (DARS). Citeseer (2004)

    Google Scholar 

  9. Morlok, R., Gini, M.: Dispersing robots in an unknown environment. Distrib. Auton. Robot. Syst. 6, 253–262 (2007)

    Google Scholar 

  10. Pinciroli, C., Trianni, V., O’Grady, R., Pini, G., Brutschy, A., Brambilla, M., Mathews, N., Ferrante, E., Di Caro, G., Ducatelle, F., Birattari, M., Gambardella, L.M., Dorigo, M.: ARGoS: a modular, parallel, multi-engine simulator for multi-robot systems. Swarm Intell. 6(4), 271–295 (2012)

    Article  Google Scholar 

  11. Roberts, J.F., Stirling, T.S., Zufferey, J.C., Floreano, D.: 2.5 d infrared range and bearing system for collective robotics. In: IEEE/RSJ International Conference on Intelligent Robots and Systems. IROS 2009, pp. 3659–3664. IEEE (2009)

    Google Scholar 

  12. Şahin, E.: Swarm robotics: from sources of inspiration to domains of application. In: Şahin, E., Spears, W.M. (eds.) SR 2004. LNCS, vol. 3342, pp. 10–20. Springer, Heidelberg (2005). doi:10.1007/978-3-540-30552-1_2

    Chapter  Google Scholar 

  13. Schwager, M., Rus, D., Slotine, J.J.: Decentralized, adaptive coverage control for networked robots. Int. J. Robot. Res. 28(3), 357–375 (2009)

    Article  Google Scholar 

  14. Spears, W.M., Spears, D.F., Hamann, J.C., Heil, R.: Distributed, physics-based control of swarms of vehicles. Auton. Robots 17(2), 137–162 (2004)

    Article  Google Scholar 

  15. Svennebring, J., Koenig, S.: Building terrain-covering ant robots: a feasibility study. Auton. Robots 16(3), 313–332 (2004)

    Article  Google Scholar 

  16. Tan, Y., Zheng, Z.Y.: Research advance in swarm robotics. Defence Technol. 9(1), 18–39 (2013)

    Article  Google Scholar 

  17. Zheng, Z., Tan, Y.: Group explosion strategy for searching multiple targets using swarm robotic. In: 2013 IEEE Congress on Evolutionary Computation (CEC), pp. 821–828. IEEE (2013)

    Google Scholar 

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Acknowledgement

This work was supported by the Natural Science Foundation of China (NSFC) under grant no. 61375119 and the Beijing Natural Science Foundation under grant no. 4162029, and partially supported by the Natural Science Foundation of China (NSFC) under grant no. 61673025, and National Key Basic Research Development Plan (973 Plan) Project of China under grant no. 2015CB352302.

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Authors and Affiliations

  1. Key Laboratory of Machine Perception (MOE), and Department of Machine Intelligence School of Electronics Engineering and Computer Science, Peking University, Beijing, 100871, China

    Xiangyu Liu & Ying Tan

Authors
  1. Xiangyu Liu
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  2. Ying Tan
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Corresponding author

Correspondence to Ying Tan .

Editor information

Editors and Affiliations

  1. Peking University, Beijing, China

    Ying Tan

  2. Kyushu University, Fukuoka, Japan

    Hideyuki Takagi

  3. Southern University of Science and Technology, Shenzhen, China

    Yuhui Shi

  4. Shenzhen University, Shenzhen, China

    Ben Niu

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Liu, X., Tan, Y. (2017). Adaptive Potential Fields Model for Solving Distributed Area Coverage Problem in Swarm Robotics. In: Tan, Y., Takagi, H., Shi, Y., Niu, B. (eds) Advances in Swarm Intelligence. ICSI 2017. Lecture Notes in Computer Science(), vol 10386. Springer, Cham. https://doi.org/10.1007/978-3-319-61833-3_16

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  • DOI: https://doi.org/10.1007/978-3-319-61833-3_16

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-61832-6

  • Online ISBN: 978-3-319-61833-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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